Facial images of individuals convey large quantities of demographic facial information, such as emotion, expression, identity, age, gender, ethnicity, etc. As such, facial image analysis is important in a wide variety of applications in multiple areas, such as security, law-enforcement, entertainment, a human-computer interaction (HCI) system, and artificial intelligence (AI) systems.
There are different types of facial image analysis tasks, such as face verification and age estimation. Some facial regions may be more important to some facial image analysis tasks compared to other facial regions. For example, for age estimation, facial regions with age variation are analyzed. By comparison, for face verification, age-invariant facial regions (e.g., eyes, nose, mouth, etc.) are analyzed. Facial regions with age variations (e.g., forehead with wrinkles, etc.), however, are not helpful for face verification as conflicting facial patterns may arise between age and identity. Facial images that show age variations in certain facial regions typically lead to increased differences in facial appearance, thereby increasing the difficulty at which face verification with age changes (i.e., face matching between facial images showing large age gaps) is performed. One conventional solution for improving cross-age face recognition is face synthesis (i.e., face modeling). In face synthesis, an input facial image is synthesized to a target age. It is very difficult, however, to accurately synthesize and simulate an unpredictable aging progress if aging patterns are unknown. Another conventional solution for improving cross-age face recognition is automatically identifying age-invariant facial regions.
Conventionally, as the importance of certain facial regions varies for different facial image analysis tasks, different facial image analysis tasks are preformed utilizing different distinctive frameworks. There does not exist an existing framework that can handle multiple conflicting facial image analysis tasks.